Twelve months since the first dev log. Time to be honest about what worked, what surprised me, and what remains to be solved.
What Worked
The core thesis validated. A complete, longitudinal record of coherent decision-making can be transformed into a functional decision support system that genuinely scales judgment. The holdout experiment demonstrated 85-90% decision alignment in core domains. Daily use confirmed that the efficiency gains are real and compound over time.
The lattice architecture was the right bet. Organizing decisions by structural similarity rather than chronology or domain enables the cross-domain retrieval that makes Tessera valuable for a polymath. The meta-heuristics, not the domain expertise, are the primary value.
The air-gap and local-LLM decisions were vindicated by every conversation I have had about AI governance this year. The organizations I advise are moving toward data sovereignty. Tessera was built there from day one.
What Surprised Me
The fiction corpus impact was larger than expected. The journal entries are more valuable than the emails. The self-correction data is the secret ingredient that most people would overlook. And the second-order efficiency gains, the quieter days, are where the majority of the value actually lives.
What Remains
Real-time context integration. Tessera works from explicit input today. She cannot observe a meeting, read body language, or sense the political dynamics in a room. Multi-user deployment, the architecture for organizational use rather than personal use, is designed but not tested. And the question of how Tessera’s principles apply to preserving other polymaths’ judgment is open and fascinating.
Tessera is real. She works. She scales me without losing me. Year one proved the concept. Year two builds on it.